The present invention relates generally to cognitive radios, as implemented in software defined receivers, and more specifically to a new hybrid signal detection system and method for dynamic spectrum access.
Radio frequency spectrum space is typically allocated by government agencies by license, with licensees being allocated a fixed frequency band for their exclusive use, whether or not those frequency bands are being used at all times.
With each modern new use for spectrum space, there is an increased demand for increasingly unavailable spectrum space.
Dynamic spectrum access (DSA) promises a solution to this spectrum allocation problem by spectrum sharing. Spectrum sharing potentially allows unlicensed users, called Secondary Users (SUs), to opportunistically access unoccupied/under-utilized spectrum licensed to incumbent users, called Primary Users (PUs). Dynamic spectrum sharing can alleviate the problem of inefficient spectrum utilization and scarcity.
Proposed dynamic spectrum access technologies will use so-called cognitive radios.
A cognitive radio is an “intelligent” radio that can be programmed and dynamically configured to detect available channels in wireless spectrum, and change its transmission and reception parameters to allow more concurrent wireless communications in a given spectrum or frequency band at a location.
The goal of a cognitive radio is being met by the development of software defined radios.
A software defined radio, instead of all the individual components of a typical analog, or even partly digital, radio transmitter or receiver, attempts to place all or most of the complex signal handling into a modern digital format. In its simplest form, a software defined radio comprises an antenna connected to an analog-to-digital converter chip connected to conventional computer circuitry so that all the filtering and signal detection can take place in the digital domain. FIG. 1 shows a typical prior art software defined receiver architecture, in which a programmable signal processor 10 can be implemented in field programmable gate arrays (FPGA), digital signal processors (DSP) and even in general purpose processors (GPP).
The key enabling technology for dynamic spectrum access to work, as implemented in software defined radios, is spectrum sensing.
Spectrum sensing, or signal detection, determines the presence or absence of spectrum holes for opportunistic access. That is, Secondary Users are granted, or simply take, access to spectrum after it is determined that no Primary User activity is detected in a frequency band of interest. In addition to identifying spectrum holes, a sensing function provides interference protection to PUs by preventing, or discouraging, SUs from accessing the spectrum or frequency band while a PU is actively using it.
While various signal processing techniques have been proposed in the prior art for spectrum sensing, energy based sensing is commonly used for its simplicity and ease of practical implementation. Two commonly used energy detectors schemes are block energy detection and sequential energy detection.
Block energy detectors determine the presence or absence of signals of interest by processing a block, that is, a large sample size taken from a spectrum space, or frequency band, estimating the signal energy present in that spectrum space and making a yes-no decision on whether there is a PU signal present or an available spectrum hole based on the idea that sufficiently high energy indicates that there must be a Primary User (PU) signal over and above the noise, and a sufficiently low signal energy indicates that there must be only noise and thus a “spectrum hole.” Sample size is a function of sampling duration and bandwidth of the sampling function. In general, a block energy detector is characterized by a fixed detection delay since the sensing duration and bandwidth are usually fixed parameters. Therefore, block energy detection can provide reliable sensing at a fixed detection delay. In a congested spectrum environment where spectrum availability is highly dynamic, it is crucial to not only reliably detect presence of spectrum holes, but also quickly. As the sample size, a resulting delay parameter is set to a fixed value, block based energy detection is not suitable for most practical DSA systems.
Sequential energy detectors determine the presence or absence of signals of interest by processing a single, or very small, sample and making a yes-no-unknown decision. If the single sample size results in neither a clear yes or no, another sample is collected and processed until a yes or no decision can be made. Generally, sequential energy detectors provide the quickest reliable detection while providing bounded false alarm (FA) and missed detection (MD) probabilities. Quickest detection means that a sequential energy detector requires a reduced number of samples (on average) to yield performance comparable to a block energy detector. The term detection delay refers to the number of samples required to provide reliable detection.
In a theoretical setting, a sequential detector continues to take additional samples until a reliable decision is made. That is, the sequential detector terminates if and only if a decision is made, not only increasing detection time, but also unpredictably increasing detection time.
The prior art is replete with statistical approaches for making determinations based on decision statistics such as the estimated signal energy in a set of samples collected from a spectrum space. The sequential probability ratio test (SPRT), introduced in the 1940s in Abraham Wald, Sequential Analysis, John Wiley and Sons, Inc. (1947), for example, has been proposed for use for cognitive radios. SPRT, however, despite its wide use in many scientific and engineering fields, and that for given detection error probabilities, generally requires the smallest average number of samples for a given detection performance, introduces computational complexities from requiring computation of likelihood ratios, which are difficult to implement in a cognitive radio and, under conditions likely to arise in spectrum sensing, can actually increase error probabilities.
There is, therefore, a need for faster and more reliable spectrum sensing systems and methods to successfully enable dynamic spectrum access as implemented in cognitive radio, software defined radios or any other system where spectrum sensing is required. Such new spectrum sensing systems and methods must be computationally efficient and able to be efficiently implemented in existing digital signal processing hardware.
There is an additional need for spectrum sensing systems and methods that are easily adaptable and reconfigurable as needed for different dynamic spectrum access purposes.